The Rainfall Annual Cycle Bias over East Africa in CMIP5 Coupled Climate Models
East Africa has two rainy seasons: the long rains (March–May, MAM) and the short rains (October–December, OND). Most CMIP3/5 coupled models overestimate the short rains while underestimating the long rains. In this study, the East African rainfall bias is investigated by comparing the coupled historical simulations from CMIP5 to the corresponding SST-forced AMIP simulations. Much of the investigation is focused on the MRI-CGCM3 model, which successfully reproduces the observed rainfall annual cycle in East Africa in the AMIP experiment but its coupled historical simulation has a similar but stronger bias as the coupled multimodel mean. The historical\(-\)AMIP monthly climatology rainfall bias in East Africa can be explained by the bias in the convective instability (CI), which is dominated by the near surface moisture static energy (MSE) and ultimately by the MSE’s moisture component. The near surface MSE bias is modulated by the sea surface temperature (SST) over the western Indian Ocean. The warm SST bias in OND can be explained by both insufficient ocean dynamical cooling and latent flux, while the insufficient short wave radiation and excess latent heat flux mainly contribute to the cool SST bias in MAM.
East Africa has been undergoing increased frequency and intensity of droughts in recent decades, raising the question of whether the drying trend will continue in a warmer future climate as forced by anthropogenic emissions of greenhouse gases (GHGs). Some studies proposed that the recent drying trend can be attributed to SST anomalies over the Indian Ocean induced by anthropogenic forcing (Funk et al., 2008; Williams et al., 2011), suggesting an extension of the current drying trend into the near future. However, there is a strong consensus in model projections from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and the more recent Coupled Model Intercomparison Project Phase 5 (CMIP5, Taylor et al. (2012)) that precipitation over East Africa will increase (Shongwe et al., 2011; Otieno et al., 2013; Otieno et al., 2013a), implying that the current dry conditions will be, at least partly, ameliorated in the near future.
The reliability of these optimistic projections on East African future hydroclimate and their suitability to serve as the foundation for the development community, however, depend on the performance of the models in reproducing past and current East African hydroclimate. By examining the performance of both SST-forced (AMIP-style) models and the coupled models used in the CMIP5 historical experiment (CMIP-style) in simulating the East African long rains (March–May, MAM), Yang et al. (2014) showed that the SST-forced models are in general able to capture the observed decadal variability of the long rains, which is primarily driven by the SST variations over the Pacific Ocean. The coupled models, which are used for the 21st century climate projections, however, generally fail to capture the correct long rains-SST relationship on decadal or longer time scales. Moreover, the coupled models misrepresent the East African rainfall annual cycle by overestimating the short rains (October–December, OND) and underestimating the long rains, which has also been reported for the CMIP3 coupled models (Anyah et al., 2012). The cause of the rainfall annual cycle bias in the CMIP5 coupled models and its implications for the projections from these coupled models are still not clear.